A novel improved full vector spectrum algorithm and its application in multi-sensor data fusion for hydraulic pumps. (February 2019)
- Record Type:
- Journal Article
- Title:
- A novel improved full vector spectrum algorithm and its application in multi-sensor data fusion for hydraulic pumps. (February 2019)
- Main Title:
- A novel improved full vector spectrum algorithm and its application in multi-sensor data fusion for hydraulic pumps
- Authors:
- Yu, He
Li, Hongru
Li, Yaolong
Li, Yifan - Abstract:
- Highlights: A novel time-frequency analysis approach based on EWT-VCR. First application of full vector spectrum improved by EWT-VCR in pumps' data fusion. The novel feature FVFE to efficiently describe pumps' degradation process. Two case studies highlighting good performance of the proposed methodology. Abstract: The full vector spectrum is an effective and efficient tool for homologous multi-sensor data fusion in rotating machinery. However, this methodology just takes Fourier Transform to obtain the harmonic trajectory information hidden in multi-sensor data and it has some drawbacks of processing nonlinear, multi-frequency and noise-containing data. To address this critical issue, this paper provides a novel approach called EWT-VCR based on Empirical Wavelet Transform (EWT) and Variance Contribution Rate (VCR) to improve the adaptability and accuracy of the fusion method. EWT is introduced as a signal preprocessing technique to decompose complex signals into variable frequency bands. And VCR is proposed to denoise, fuse EWT components at different frequency bands, and enhance useful harmonic components. The full vector spectrum technology is utilized to carry out the full vector information fusion of the improved multi-sensor signals for further spectrum analysis. The proposed methodology is applied to multi-channel vibration signal fusion for hydraulic pumps to detect specific frequencies related to pump's degradation process and a novel degradation feature named FullHighlights: A novel time-frequency analysis approach based on EWT-VCR. First application of full vector spectrum improved by EWT-VCR in pumps' data fusion. The novel feature FVFE to efficiently describe pumps' degradation process. Two case studies highlighting good performance of the proposed methodology. Abstract: The full vector spectrum is an effective and efficient tool for homologous multi-sensor data fusion in rotating machinery. However, this methodology just takes Fourier Transform to obtain the harmonic trajectory information hidden in multi-sensor data and it has some drawbacks of processing nonlinear, multi-frequency and noise-containing data. To address this critical issue, this paper provides a novel approach called EWT-VCR based on Empirical Wavelet Transform (EWT) and Variance Contribution Rate (VCR) to improve the adaptability and accuracy of the fusion method. EWT is introduced as a signal preprocessing technique to decompose complex signals into variable frequency bands. And VCR is proposed to denoise, fuse EWT components at different frequency bands, and enhance useful harmonic components. The full vector spectrum technology is utilized to carry out the full vector information fusion of the improved multi-sensor signals for further spectrum analysis. The proposed methodology is applied to multi-channel vibration signal fusion for hydraulic pumps to detect specific frequencies related to pump's degradation process and a novel degradation feature named Full Vector Factor Entropy (FVFE) is extracted to describe hydraulic pump's degradation process during its life cycle. The effectiveness of the proposed methods is validated through two experimental cases. … (more)
- Is Part Of:
- Measurement. Volume 133(2019)
- Journal:
- Measurement
- Issue:
- Volume 133(2019)
- Issue Display:
- Volume 133, Issue 2019 (2019)
- Year:
- 2019
- Volume:
- 133
- Issue:
- 2019
- Issue Sort Value:
- 2019-0133-2019-0000
- Page Start:
- 145
- Page End:
- 161
- Publication Date:
- 2019-02
- Subjects:
- Hydraulic pump -- Data fusion -- Full vector spectrum -- EWT -- VCR -- Degradation feature
Weights and measures -- Periodicals
Measurement -- Periodicals
Measurement
Weights and measures
Periodicals
530.8 - Journal URLs:
- http://www.sciencedirect.com/science/journal/02632241 ↗
http://www.elsevier.com/journals ↗ - DOI:
- 10.1016/j.measurement.2018.10.011 ↗
- Languages:
- English
- ISSNs:
- 0263-2241
- Deposit Type:
- Legaldeposit
- View Content:
- Available online (eLD content is only available in our Reading Rooms) ↗
- Physical Locations:
- British Library DSC - 5413.544700
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